Vera Demberg
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The paper introduces MuPHI, a dataset and MuPHIRM, a reasoning-augmented training framework, to improve Vision-Language Models' ability to detect and reason about subtle, context-dependent multimodal harm.
The paper introduces semantic motion anchors, a method that bridges the gap between spoken text and gesture meaning by providing structured, semantically grounded supervision, significantly improving co-speech gesture retrieval.
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MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization
Anisha Saha, Varsha Suresh, Teodora Kamova, Sophia Wiedmann +2 more
The paper introduces MuPHI, a dataset and MuPHIRM, a reasoning-augmented training framework, to improve Vision-Language Models' ability to detect and reason about subtle, context-dependent multimodal…